Process-metrology reproducibility bands for lithographic photomasks

ABSTRACT

A photomask lithography simulation model is created for making a semiconductor chip. Poor metrology is filtered and removed from a contour-specific metrology dataset to improve performance of the photomask. Filtering is performed by the application of a weighting scheme.

BACKGROUND

The present invention relates generally to the field ofphotolithography, and more particularly to micro-fabrication processes.

Electronic circuit simulation uses mathematical models to replicate thebehavior of an actual electronic device or circuit. For integratedcircuit (IC) design, electronic design automation (EDA) tools are oftenused. Simulation software allows for modeling of circuit operation andis an invaluable analysis tool. Simulating a circuit's behavior beforeactually building it improves design efficiency by revealing faultydesigns and providing insight into the behavior of electronic circuitdesigns. In particular, for integrated circuits, the tooling (photomask)is expensive, breadboards are impractical, and probing the behavior ofinternal signals is difficult. Therefore, almost all IC designactivities rely on circuit simulation.

Photolithography, also known as optical lithography, or UV lithography,is a process used in micro-fabrication to pattern parts of a thin film,or the bulk of a substrate. It uses light to transfer a geometricpattern from a photomask to a light-sensitive chemical “photoresist” onthe substrate.

Mask data preparation (MDP) is the procedure of translating a filecontaining the intended set of polygons from an integrated circuitlayout into set of instructions that a photomask writer can use togenerate a physical mask. MDP usually involves mask fracturing wherecomplex polygons are translated into simpler shapes, often rectanglesand trapezoids, that can be handled by the mask writing hardware. RecentMDP procedures require the additional steps of resolution enhancementtechnologies (RET) and/or optical proximity correction (OPC) with afocus on design for manufacturability.

Optical proximity correction (OPC) is a photolithography enhancementtechnique commonly used to compensate for image errors due todiffraction, or process effects. The need for OPC often arises in themaking of semiconductor devices due to the limitations of light tomaintain the edge placement integrity of the original design, afterprocessing, into the etched image on the silicon wafer.

OPC and verification model accuracy depend on having a large number ofdata points for calibration. The requirement of a large number of datapoints drives up process costs, but the results are precise. Forexample, OPC models use optics (physical) and empirical resist models tobalance the accuracy and speed of processing, which leads to a highdependency on the amount of data collected, the relevance of the data toall design constructs that are going to be placed on the mask, theprecision of metrology, and the SEM (scanning electron microscopy)offsets to physical data. Conventional manufacturability efforts, suchas OPC are confronted with the negative effects associated with theenormous amounts of data they can produce (too much data can sometimesbecome a problem for the mask writer to be able to create a mask in areasonable amount of time).

Contour tracing, also known as border following or boundary following,is a technique that is applied to digital images in order to extracttheir boundary (referred to herein as “contour extraction.” Once thecontour of a given pattern is extracted, its characteristics may beexamined and used as features for use in pattern classification. It isknown to extract and encode the boundary points of contours.

SUMMARY

In one aspect of the present invention, a method, a computer programproduct, and a system for creating a lithography simulation modelincludes: (i) generating a plurality of SEM (scanning electronmicroscopy) metrology datasets corresponding to a target contour of anintegrated circuit design; (ii) determining an average contour based ona filtered subset of the plurality of SEM metrology datasets; (iii)computing an image parameter for a set of gauges for the target contour;(iv) correlating the image parameter and a process-metrology range (PMR)to generate a parameter to PMR correlation; (v) determining a samplingcount for the target contour based at least in part on the parameter toPMR correlation; (vi) computing an image log-scope (ILS) value for eachgauge in the set of gauges; and (vii) generating a weight function forthe target contour based at least in part on a PMR variance and the ILSvalue. The filtered subset excludes unphysical excursions of the targetcontour.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic view of a first embodiment of a system accordingto the present invention;

FIG. 2 is a flowchart showing a method performed, at least in part, bythe first embodiment system;

FIG. 3 is a schematic view of a machine logic (for example, software)portion of the first embodiment system;

FIG. 4 is a flowchart view of a second embodiment of a method accordingto the present invention;

FIG. 5 is a diagram view showing information that is generated by and/orhelpful in understanding embodiments of the present invention;

FIG. 6 is a flowchart view of a third embodiment of a method accordingto the present invention;

FIG. 7 is a graph view showing information that is generated by and/orhelpful in understanding embodiments of the present invention; and

FIGS. 8A and 8B are screenshots showing information that is generated byand/or helpful in understanding embodiments of the present invention.

DETAILED DESCRIPTION

A photomask model is created for making a semiconductor chip. Poormetrology is filtered and removed from a contour-specific metrologydataset to improve performance of the photomask. Filtering is performedby the application of a weighting scheme. The present invention may be asystem, a method, and/or a computer program product. The computerprogram product may include a computer readable storage medium (ormedia) having computer readable program instructions thereon for causinga processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium, or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network, and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network, and forwards the computer readableprogram instructions for storage in a computer readable storage mediumwithin the respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computer,or entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture, including instructions which implement aspectsof the function/act specified in the flowchart and/or block diagramblock or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions, or acts, or carry out combinations of special purposehardware and computer instructions.

The present invention will now be described in detail with reference tothe Figures. FIG. 1 is a functional block diagram illustrating variousportions of networked computers system 100, in accordance with oneembodiment of the present invention, including: process-metrologyreproducibility (PMR) band sub-system 102; client sub-systems 104, 106,108, 110, 112; communication network 114; PMR band computer 200;communication unit 202; processor set 204; input/output (I/O) interfaceset 206; memory device 208; persistent storage device 210; displaydevice 212; external device set 214; random access memory (RAM) devices230; cache memory device 232; and PMR band program 300.

Sub-system 102 is, in many respects, representative of the variouscomputer sub-system(s) in the present invention. Accordingly, severalportions of sub-system 102 will now be discussed in the followingparagraphs.

Sub-system 102 may be a laptop computer, tablet computer, netbookcomputer, personal computer (PC), a desktop computer, a personal digitalassistant (PDA), a smart phone, or any programmable electronic devicecapable of communicating with the client sub-systems via network 114.Program 300 is a collection of machine readable instructions and/or datathat is used to create, manage, and control certain software functionsthat will be discussed in detail below.

Sub-system 102 is capable of communicating with other computersub-systems via network 114. Network 114 can be, for example, a localarea network (LAN), a wide area network (WAN) such as the Internet, or acombination of the two, and can include wired, wireless, or fiber opticconnections. In general, network 114 can be any combination ofconnections and protocols that will support communications betweenserver and client sub-systems.

Sub-system 102 is shown as a block diagram with many double arrows.These double arrows (no separate reference numerals) represent acommunications fabric, which provides communications between variouscomponents of sub-system 102. This communications fabric can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware component within a system. For example,the communications fabric can be implemented, at least in part, with oneor more buses.

Memory 208 and persistent storage 210 are computer readable storagemedia. In general, memory 208 can include any suitable volatile ornon-volatile computer readable storage media. It is further noted that,now and/or in the near future: (i) external device(s) 214 may be able tosupply, some or all, memory for sub-system 102; and/or (ii) devicesexternal to sub-system 102 may be able to provide memory for sub-system102.

Program 300 is stored in persistent storage 210 for access and/orexecution by one or more of the respective computer processors 204,usually through one or more memories of memory 208. Persistent storage210: (i) is at least more persistent than a signal in transit; (ii)stores the program (including its soft logic and/or data), on a tangiblemedium (such as magnetic or optical domains); and (iii) is substantiallyless persistent than permanent storage. Alternatively, data storage maybe more persistent and/or permanent than the type of storage provided bypersistent storage 210.

Program 300 may include both machine readable and performableinstructions, and/or substantive data (that is, the type of data storedin a database). In this particular embodiment, persistent storage 210includes a magnetic hard disk drive. To name some possible variations,persistent storage 210 may include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 210 may also be removable. Forexample, a removable hard drive may be used for persistent storage 210.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage210.

Communications unit 202, in these examples, provides for communicationswith other data processing systems or devices external to sub-system102. In these examples, communications unit 202 includes one or morenetwork interface cards. Communications unit 202 may providecommunications through the use of either, or both, physical and wirelesscommunications links. Any software modules discussed herein may bedownloaded to a persistent storage device (such as persistent storagedevice 210) through a communications unit (such as communications unit202).

I/O interface set 206 allows for input and output of data with otherdevices that may be connected locally in data communication withcomputer 200. For example, I/O interface set 206 provides a connectionto external device set 214. External device set 214 will typicallyinclude devices such as a keyboard, keypad, a touch screen, and/or someother suitable input device. External device set 214 can also includeportable computer readable storage media such as, for example, thumbdrives, portable optical or magnetic disks, and memory cards. Softwareand data used to practice embodiments of the present invention, forexample, program 300, can be stored on such portable computer readablestorage media. In these embodiments, the relevant software may (or maynot) be loaded, in whole or in part, onto persistent storage device 210via I/O interface set 206. I/O interface set 206 also connects in datacommunication with display device 212.

Display device 212 provides a mechanism to display data to a user andmay be, for example, a computer monitor or a smart phone display screen.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of the presentinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus the presentinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

PMR band program 300 operates to support a modeler in creating aphotomask lithography simulation model for making a semiconductor chip.In some embodiments, poor metrology is filtered and removed from acontour-specific metrology dataset. Filtering is performed by theapplication of a weighting scheme.

Some embodiments of the present invention recognize the following facts,potential problems and/or potential areas for improvement with respectto the current state of the art: (i) collecting large amounts of data isexpensive; (ii) the weighting of one-dimensional constructs (1D) isdiscrepantly higher than two-dimensional constructs (2D) due to the lackof confidence in the 2D measurements; and/or (iii) no method isavailable to automatically sort out bad data points before they are sentto the modeler, which leads to much time and effort spent separatinggood and bad data points.

FIG. 2 shows flowchart 250 depicting a first method, according to thepresent invention. FIG. 3 shows program 300 for performing at least someof the method steps of flowchart 250. This method and associatedsoftware will now be discussed, over the course of the followingparagraphs, with extensive reference to FIG. 2 (for the method stepblocks) and FIG. 3 (for the software blocks).

Processing begins at step S255, where extract contours module “mod” 355,extracts a set of contours from a set of SEM (scanning electronmicroscopy) images on a photomask. Extracting contours from a set of SEMimages is performed by one of any number of conventional programs, suchas DesignGauge Analyzer. (Note: the term(s) “DesignGauge” and“DesignGauge Analyzer” may be subject to trademark rights in variousjurisdictions throughout the world and are used here only in referenceto the products or services properly denominated by the marks to theextent that such trademark rights may exist.)

Processing proceeds to step S260, where gauges mod 360 identifies a setof gauges. Identification of the gauges is based on fragmentationthrough the use of a relatively simple model, such as a constantthreshold model, where more sampling points are placed in areas of highfrequency, such as line ends and inside corners, and fewer, or sparse,sampling points are placed in areas of low frequency, such as parallellines.

Processing proceeds to step S265, where metrology datasets mod 365generates SME metrology datasets for the set of contours extracted instep S255. The metrology datasets include measurements of key dimensionsassociated with the set of gauges. The individual metrology results arestored as metrology datasets.

Processing proceeds to step S270, where average contour mod 370determines an average metrology range associated with a given contour todefine an average contour.

Processing proceeds to step S275, where image parameter mod 375 computesthe image parameter value(s) for the set of gauges.

Processing proceeds to step S280, where correlation mod 380 correlatesimage parameter value(s) and process-metrology reproducibility (PMR)band(s). By correlating the computed image parameter values, such as1/ILS and image contrast, with corresponding filtered contourprocess-metrology reproducibility band(s), a sampling number, orsampling count, can be determined. The filtered contour PMR bandsprovide more reliable data on which to determine the sample numberbecause the unphysical excursions and other sources of noise arefiltered out.

Processing proceeds to step S285, where sampling number mod 385determines a sampling number for each contour identified in step S260.The sampling number is tailored to the specified contour according tothe corresponding PMR(s).

Processing proceeds to step S290, where image log-slope (ILS) mod 390computes the ILS for each gauge. Gauge placement varies by construct andconstruct partition. In this embodiment, weight is assigned according toPMR variation measured over a PMR band. In some embodiments, the ILScorrelation to PMR variation is used to assign weights according todegree of difficulty in both metrology and modeling.

Processing proceeds to step S295, where weight function mod 395generates weight functions for the set of contours. In this example, arelative weighting function is generated on a per-target-type basis aswell as a per-slice-of-target basis. Alternatively, only one of the tworelative weighting functions are generated.

Processing ends at step S298, where simulation mod 398 simulates thefiltered and weighted contours. The lithography simulation is performedby a conventional simulation sub-system, or program. While processingends in this example, processing may return to step S275, where imageparameter(s) are computed.

Further embodiments of the present invention are discussed in theparagraphs that follow and later with reference to the remainingfigures.

Some embodiments of the present invention provide for a method ofdetermining an appropriate number of contours to be collected whosevariability is measured by means of a PMR. A weight, based on thegeometrical and image parameter description, is associated with eachsection, or slice, in the shape, or contour, to be considered forcalibration. Further, the number of repeats are established, based onthe total variability in PMR measurement for satisfactorily categorizingthe selected pattern in terms of metrology difficulty, that is, in termsof the variability in measurement of the same geometry multiple times.In that way, a correlation is established between the appropriate numberof contours and the difficulty in printing the selected pattern.

FIG. 4 shows a flowchart for contour-based design process 400. Thecontour-based design process follows conventional steps including:design layout S402, automatic recipe creation S404, implementing therecipe S406, measurement using critical dimension (CD) characteristicswith a SEM S408, capturing SEM images of the photomask S410, and contourextraction S412.

Some embodiments of the present invention use information from theprocess-metrology reproducibility (PMR) band to select contours in lieuof numerous measurements required conventionally. This includesgenerating the PMR band as well as cleaning and sorting the data.

Processing the set of SEM contours extracted in step S412 begins at stepS414, where the average SEM contour is determined with respect to aparticular target contour from the set of SEM contours. Step S414 alsodetermines a PMR band for each construct type.

Some embodiments of the present invention merge individual metrologydatasets into one metrology dataset, where the individual dataset namesare carried over to layer names for individual contour layers. In thatway, a process-metrology reproducibility (PMR) band is generated, andthe PMR band is output along with the average contours as layers for usein contour analysis.

The average SEM contour is processed at step S416, where filtering andweighting analysis are performed. The filtering process involvesremoving any unphysical excursions from the dataset used in determiningthe average contour. The weighting process, as discussed herein,includes generating cutline and determining the ILS for eachcorresponding construct. Variance input used in weighting includes PMRvariation gauges, where the weight is decreased for increased PMRvariation. The weight function is generated to account for whether theconstruct, or contour, is two-dimensional or one-dimensional.

The filtering and weighting analysis step is discussed in more detailbelow. To summarize, the output from step S416 is an estimate, for eachSEM contour, for the number of samples needed for the desired precisionand an estimate of the PMR band at a practical number of samplingpoints. Processing, according to process 400 proceeds to step S418,where the output from step S416 is input into a EDA simulator.Processing proceeds down one of two paths. The first path, 1, proceedsto step S422, where a set of simulated contours is produced. Thesimulated contours return to step S416 for repeated processing. Thesecond path, 2, returns to step S402, where the refined model is usedfor producing a next design layout.

Some embodiments of the present invention use PMR classification todetermine a weighting of selected contours and/or corresponding slices.Additionally, the PMR supports estimation of the number of repeatmeasurements to make for a given contour. The number of repeatmeasurements refers to the number of times the same geometry should bemeasured in order to confidently use the measurement(s) in a modelcalibration suite. By checking the distribution spread of the PMR bandwhen each geometrical construct is measured, for example, 8, 7, 6, 5,and 4 times, a saturation point is established. The saturation pointrefers to the point where the least number of measurements of acharacteristic are made to capture the maximum variability of thatcharacteristic. That is to say, if the number of measurements isdecreased below the saturation point, variability of the characteristicwill be poorly captured. For some constructs, a high number ofmeasurements, referred to herein as “repeat measurements,” are required.

Some embodiments of the present invention are directed to 2D contoursampling according to target type. Target types include: (i) ends; (ii)sides; (iii) side spacing; (iv) side width; (v) end to side; (vi)L-shape (concave corner); (vii) T-shape; (viii) H-shape; (ix) isolatedholes (e.g., square-shape); and/or (x) elongated holes (e.g. short bar).Additionally, partial geometry may represent a target type that can bemarked and measured according to some embodiments of the presentinvention. An example process for 2D contour sampling by target typeincludes: (i) place sites based on fragmentation from simple model form;(ii) sort by construct type (pattern match); (iii) generate gauges; (iv)compute image parameters per gauge; (v) correlate image parameters andPMR, for example, 1/ILS vs. PMR variability; (vi) need plot here; (vii)determine sampling number per target by PMR estimation; and (viii)increase sampling with PMR variation.

Some embodiments of the present invention are directed to 2D SEM(scanning electron microscopy) contour filtering and metrology (metro)variability. In this example, individual SEM metro datasets are mergedinto one metro dataset. The merged datasets carry individual datasetnames to the layer names for each individual contour layer. The exampleprocess then generates a SEM “MetroBand.” The SEM metroband is a rangeof values for a given metrology for a given construct type. In thisexample, the metrics for filtering are determined, and any unphysicalexcursion from average contour is filtered out. An example formula fordetermining an appropriate unphysical excursion, E, follows:

${E = \left( \frac{\begin{matrix}{{{MAXIMUM}\mspace{14mu}{CONTOUR}\mspace{14mu}{VALUE}} -} \\{{MINIMUM}\mspace{14mu}{CONTOUR}\mspace{14mu}{VALUE}}\end{matrix}}{{AVERAGE}\mspace{14mu}{CONTOUR}\mspace{14mu}{VALUE}} \right)},$where: maximum contour value is measured from outside to outside of thePMR band; and minimum contour value is measured inside to inside of thePMR band (the minimum expected measurement for the shape, or contourgeometry; and average contour value is a measurement taking into accounteach metrology contour collected and used to measure variability.

Having filtered out selected unphysical excursions, variance input istaken into account for weighting. It should be noted that someembodiments of the present invention predict failure mechanisms such asa short, or an open, condition based on the amount of deviation outsideand/or inside from the PMR band. Variance input includes binned PMRvariation gauges. The weighting is adjusted downward as PMR variationincreases.

Some embodiments of the present invention take the following stepsassociated with variance analysis to filter out noise in contours and/orto apply weighting to a contour target: (i) merge individual metrologycontours for a given target contour to generate a PMR band for thecontour; (ii) clean noise from merged contours; (iii) place gaugemarkers on uniformly centered geometry of the merged contours; (iv)determine a PMR variance from a PMR average with reference to themaximum and the minimum contour values of the merged and cleanedcontours; (v) bin the remaining merged contours as a function ofdeviation from the PMR average (for the merged contours) and across PMRtarget values to be used in determining the number of contours needed toestimate the number of repeat measurements for the contour target; and(vi) simulate gauged contours and analyze comparative performance. Thesimulated gauged contours may be used in determining a contour weightingstrategy. A formula relating the PMR band to the number of SEM contoursfollows:

${\frac{1}{\left. \sqrt{}n \right.} = {{PMR}\mspace{14mu}{Band}}},$where, n=number of SEM contours.

Some embodiments of the present invention rely on a correlation betweenthe PMR band and 1/ILS. This correlation provides insight into therelationship between contours that are difficult to simulate andmetrology noise from the contour-forming algorithm. This correlation isbased on the ability to clean the PMR of noise coming from the contourforming algorithm, such as long acute angles, and the ability to removeincomplete, or bad, PMRs for constructs for which there is a goodalternative PMR.

Based on the PMR band and ILS estimation, some embodiments of thepresent invention provide one, or both of: (i) an estimate of the numberof sampling points needed for the desired level of precision; and/or(ii) an estimate of the PMR band given a practical number of samplingpoints.

Some embodiments of the present invention are directed to a methodologyfor using contours collected by metrology tools, as discussed herein,for data filtering by selecting a minimized set of data points, for both1D and 2D design space, for model calibration along with establishing anadequate weighting scheme for model performance.

Some embodiments of the present invention are directed to a methodologyfor using contours collected by metrology tools, as discussed herein,for modeling lithographic photomask with a reduced data set whencompared to conventional large data sets. An example methodologyincludes generating a process-metrology reproducibility (PMR) band inshape representation and employing methods described herein to: (i)filter out noisy contours; (ii) to derive a guiding algorithm of sitesto be measured for each pattern function of variability; and (iii) toderive a weighting algorithm for various construct types.

Some embodiments of the present invention “clean” the PMRs withplacement of measurement markers. Both measurements and coordinates forall gauges are saved in a format suitable for processing, such as aspreadsheet. FIG. 5 is a graphic view of SEM contours 502, 504, 506,508, and 510. Measurement markers 512, 514, and 516 are placed accordingto area type, 2D or 1D. A dense fragmentation setting is used for 2Darea 526, while a lose fragmentation setting is used for 1D (relativelylong) lines 524.

FIG. 6 is an illustration of process 800 according to an embodiment ofthe present invention. Process 800 begins with SEM contours set 802,where six repeat measurements are taken. Average contour 804 isgenerated based on the average SEM contour at markers 806 and 808 placedon sites determined by fragmentation strategy. Simulated contour 810 isthe simulation of the same contour represented by six repeatmeasurements, contour set 802. Cut lines 812 indicate the placement ofgauges for determining ΔPV Band, discussed in more detail below withrespect to FIG. 7.

Some embodiments of the present invention are directed to 2D contourweighting by target contour type, or contour slice type. The targetcontour type is based on a set of patterns that identify the contoursfor a particular chip model. In this example, a 2D contour weightingstarts by placing sites based on fragmentation from a simple model form.The sites are sorted by construct type by pattern matching. Gauges aregenerated according to the construct types identified during the sortprocess. With the gauges generated, the method to increase samplingaccording to contour slice is practiced. In this method, the imagelog-slope (ILS) is computed for each gauge that is generated. A relativetarget weighting function is computed per target type and a slicerelative slice weighting function is computed per contour slice type.Some embodiments of the present invention perform the optional processwhere an OPC is performed according to each weighted contour slice type.Finally, variations for contour slice type, or target contour type, areanalyzed.

FIG. 7 shows chart 600 that presents contour data collected by someembodiments of the present invention. Geometric construct 606, for anelongated hole, as shown on the chart has the highest metrology spread,or range, as well as the highest delta to simulation (i.e., y-axisvalue). This indicates that geometric construct 606 is both poorlymodeled and poorly predicted. According to this classification, a lowerweight is applied to geometric construct 606 relative to otherconstructs depicted in chart 600. Vertical axis 602 is ΔPV Band innanometers (nm), which is the change in PV band shown in the followingequation:ΔPV Band=SEM PV Band −Simulated PV Band,where PV band is the process variability band, as measured over a rangeof simulation contours, for a range of focus and dose variation.Alternatively, the vertical axis is the normalized ΔPVband:

${{Normalized}\mspace{14mu}\Delta\;{PV}\mspace{14mu}{band}} = {\frac{\Delta\;{PV}\mspace{14mu}{Band}}{{Simulated}\mspace{14mu}{PV}\mspace{14mu}{Band}}.}$

Horizontal axis 604 is the pattern category corresponding with the ΔPVBand value. Pattern categories include: (i) major isometric hole; (ii)minor isometric hole; (iii) major elongated hole; (iv) minor elongatedhole; (v) SRAM hole for cutline #1; and (vi) SRAM hole for cutline #2.Alternatively, the horizontal axis is the cutline category (1D/2D-likeedge), or the ILS value at each cutline.

Some embodiments of the present invention are directed to a weightingmethod where different weights are applied to different bins of PMRwidth. Hybrid models are built using PMR weighting techniques describedherein. Measurement markers are converted to gauge cut lines to generateimage parameters, such as ILS or image contrast. Alternatively,establish ILS correlation at a given site and use the correlation indetermining weights for sites with the highest weight applied to theleast noisy PMR.

Some embodiments of the present invention are directed to a method forsampling contours to determine deviations from the average contourthrough the use of multiple cut lines in areas of high frequency, whichare generally harder to measure and to model.

Some embodiments of the present invention are directed to a method tosubstitute a manageable number of contours for multiple gauges using arandomization technique.

Some embodiments of the present invention are directed to a method toimprove photomask model accuracy by using extra information fromcontours and application of a weighting scheme that accounts for 2Dconstructs more accurately than with conventional methods.

Some embodiments of the present invention are directed to method tovalidate photoresist compact model, CM1, to aerial image (AI)differences as a valuable metric in determining model accuracy. Thephotoresist compact model is calibrated based on SEM gauges and SEMcontours. It is used to generate photomask shapes that transfer thedesign to silicon.

Some embodiments of the present invention are directed to a method toimprove deterministically a sampling function of metrology variance forthe same geometrical construct. Some embodiments of the presentinvention divide sites into the following groups: (i) sites for endwidths; and (ii) sites for sides: a) space, and b) width. Someembodiments of the present invention classify PMRs according to whetherthe contours are clean or ill-formed. The ill-formed contours areexcluded, or filtered out.

FIG. 8A shows screenshot 700 a with a measured PMR distribution beforeweighting analysis and filtering. FIG. 8B shows screenshot 700 b with ameasured PMR distribution after filtering out ill-formed contours.

Some embodiments of the present invention are directed to a weightingstrategy for difficult to create constructs in establishing averification model. In this example, higher weights are assigned todifficult constructs. Difficult constructs are defined by the following:(i) large variance in PMR band; and (ii) large change in metrology fromsimulation with a previous verification model. Assigning a higher weightto these difficult constructs improves model training to predictprintability failure of a given construct. While a model incorporatingthe described weighting scheme cannot safely be used in OPC, theweighting scheme can successfully be used to build a verification model.

Some embodiments of the present invention may include one, or more, ofthe following features, characteristics and/or advantages: (i) a methodto filter and remove poor metrology before is sent to the modeler; (ii)a method to decide on metrology quality and determine number ofmeasurements needed for each construct; (iii) a methodology for samplingand pattern selection for model calibration data using SEM contours;(iv) a method to improve model accuracy based on a weighting strategyderived from the PMR band (metrology variability) and relation to aerialimage (AI) contrast derived from image log slope calculation; (v)employs SEM contours in model calibration; (vi) feeds metrology contoursdirectly to model calibration; (vii) establishes correlations betweenmetrology variations, as captured in the PMR concept; (viii) applies aweighting factor attributed to different geometric entity; and/or (ix)builds a model for OPC and verification using contours collected bymetrology tools.

Some helpful definitions follow:

Present invention: should not be taken as an absolute indication thatthe subject matter described by the term “present invention” is coveredby either the claims as they are filed, or by the claims that mayeventually issue after patent prosecution; while the term “presentinvention” is used to help the reader to get a general feel for whichdisclosures herein that are believed as maybe being new, thisunderstanding, as indicated by use of the term “present invention,” istentative and provisional and subject to change over the course ofpatent prosecution as relevant information is developed and as theclaims are potentially amended.

Embodiment: see definition of “present invention” above—similar cautionsapply to the term “embodiment.”

and/or: inclusive or; for example, A, B “and/or” C means that at leastone of A or B or C is true and applicable.

User/subscriber: includes, but is not necessarily limited to, thefollowing: (i) a single individual human; (ii) an artificialintelligence entity with sufficient intelligence to act as a user orsubscriber; and/or (iii) a group of related users or subscribers.

Module/Sub-Module: any set of hardware, firmware and/or software thatoperatively works to do some kind of function, without regard to whetherthe module is: (i) in a single local proximity; (ii) distributed over awide area; (iii) in a single proximity within a larger piece of softwarecode; (iv) located within a single piece of software code; (v) locatedin a single storage device, memory or medium; (vi) mechanicallyconnected; (vii) electrically connected; and/or (viii) connected in datacommunication.

Computer: any device with significant data processing and/or machinereadable instruction reading capabilities including, but not limited to:desktop computers, mainframe computers, laptop computers,field-programmable gate array (FPGA) based devices, smart phones,personal digital assistants (PDAs), body-mounted or inserted computers,embedded device style computers, application-specific integrated circuit(ASIC) based devices.

What is claimed is:
 1. A method for creating a lithography simulation model comprising: generating a plurality of scanning electron microscopy (SEM) metrology datasets corresponding to a target contour of an integrated circuit design; determining an average contour based on a filtered subset of the plurality of SEM metrology datasets; computing an image parameter for a set of gauges for the target contour; correlating the image parameter and a process-metrology reproducibility (PMR) band to generate a parameter to PMR band correlation; determining a sampling count for the target contour based at least in part on the parameter to PMR band correlation; computing an image log-scope (ILS) value for each gauge in the set of gauges; generating a weight function for the target contour based at least in part on a PMR variance and the ILS value; creating a lithography simulation model based on the weight function; calibrating a photoresist compact model according to the lithography simulation model; generating photomask shapes with the photoresist compact model; and creating an integrated circuit based on the photomask shapes; wherein: the filtered subset excludes unphysical excursions of the target contour.
 2. The method of claim 1, further comprising: assigning an increased relative weight for a selected contour; wherein the selected contour is selected from a set of target contours based on specified outer limits of the PMR band and the parameter to PMR band correlation.
 3. The method of claim 1, further comprising; extracting the target contour from a set of SEM images.
 4. The method of claim 1, further comprising: identifying, based on a fragmentation setting, the set of gauges for the target contour.
 5. The method of claim 1, wherein: the step of correlating the image parameter and a PMR band includes placing a set of measurement markers according to one of a first fragmentation setting where markers are placed for a two-dimensional target contour, and a second fragmentation setting where markers are placed for a one-dimensional target contour; and the first fragmentation setting applies a more dense set of markers than the second fragmentation setting.
 6. A computer program product for creating a lithography simulation model, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a computer to cause the computer to: generate a plurality of scanning electron microscopy (SEM) metrology datasets corresponding to a target contour of an integrated circuit design; determine an average contour based on a filtered subset of the plurality of SEM metrology datasets; compute an image parameter for a set of gauges for the target contour; correlate the image parameter and a process-metrology reproducibility (PMR) band to generate a parameter to PMR band correlation; determine a sampling count for the target contour based at least in part on the parameter to PMR band correlation; compute an image log-scope (ILS) value for each gauge in the set of gauges; generate a weight function for the target contour based at least in part on a PMR variance and the ILS value; create a lithography simulation model based on the weight function; calibrate a photoresist compact model according to the lithography simulation model; generate photomask shapes with the photoresist compact model; and create an integrated circuit based on the photomask shapes; wherein: the filtered subset excludes unphysical excursions of the target contour.
 7. The computer program product of claim 6, further comprising: program instructions readable by a computer to cause the computer to extract the target contour from a set of SEM images.
 8. The computer program product of claim 6, further comprising: program instructions readable by a computer to cause the computer to identify the set of gauges for the target contour.
 9. The computer program product of claim 6, wherein: program instructions readable by a computer to cause the computer to correlate the image parameter and a PMR band to generate a parameter to PMR band correlation are further programmed to place a set of measurement markers based on a dense fragmentation setting for a two-dimensional target contour, and place a set of measurement markers based on a lose fragmentation setting for a one-dimensional target contour.
 10. The computer program product of claim 6, further comprising: program instructions readable by a computer to cause the computer to assign an increased relative weight for a selected contour; wherein: the selected contour is selected from a set of target contours based on specified outer limits of the PMR band and the parameter to PMR band correlation.
 11. A computer system for creating a lithography simulation model, the computer system comprising: a processor set; and a computer readable storage medium; wherein: the processor set is structured, located, connected, and/or programmed to run program instructions stored on the computer readable storage medium; and the program instructions include: a first set of program instructions programmed to generate a plurality of scanning electron microscopy (SEM) metrology datasets corresponding to a target contour of an integrated circuit design; a second set of program instructions programmed to determine an average contour based on a filtered subset of the plurality of SEM metrology datasets; a third set of program instructions programmed to compute an image parameter for a set of gauges for the target contour; a fourth set of program instructions programmed to correlate the image parameter and a process-metrology reproducibility (PMR) band to generate a parameter to PMR band correlation; a fifth set of program instructions programmed to determine a sampling count for the target contour based at least in part on the parameter to PMR band correlation; a sixth set of program instructions programmed to compute an image log-scope (ILS) value for each gauge in the set of gauges; a seventh set of program instructions programmed to generate a weight function for the target contour based at least in part on a PMR variance and the ILS value; an eighth set of program instructions programmed to create a lithography simulation model based on the weight function; a ninth set of program instructions programmed to calibrate a photoresist compact model according to the lithography simulation model; a tenth set of program instructions programmed to generate photomask shapes with the photoresist compact model; and an eleventh set of program instructions programed to create an integrated circuit based on the photomask shapes; wherein: the filtered subset excludes unphysical excursions of the target contour.
 12. The computer system of claim 11, further comprising: a twelfth set of program instructions programmed to extract the target contour from a set of SEM images.
 13. The computer system of claim 11, further comprising: a twelfth set of program instructions programmed to identify the set of gauges for the target contour.
 14. The computer system of claim 11, wherein: the fourth set of program instructions programmed to correlate the image parameter and a PMR band to generate a parameter to PMR band correlation are further programmed to place a set of measurement markers based on a dense fragmentation setting for a two-dimensional target contour, and place a set of measurement markers based on a lose fragmentation setting for a one-dimensional target contour.
 15. The computer system of claim 11, further comprising: a twelfth-set of program instructions programmed to assign an increased relative weight for a selected contour; wherein: the selected contour is selected from a set of target contours based on specified outer limits of the PMR band and the parameter to PMR band correlation. 